
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							a__					(							snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		[
      '''decoder.version''',
      '''decoder.output_projection.weight''',
      '''_float_tensor''',
      '''decoder.embed_positions._float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(snake_case       ,			snake_case							)


def 							a__					(							snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[Any]					=		emb.weight.shape
  __SCREAMING_SNAKE_CASE      :	str					=		nn.Linear(snake_case       ,			snake_case       ,			bias=snake_case							)
  __SCREAMING_SNAKE_CASE      :	int					=		emb.weight.data
  return lin_layer


def 							a__					(							snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	List[str]					=		torch.load(snake_case       ,			map_location='''cpu'''							)
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		Namespace(**checkpoint['''cfg''']['''model''']							)
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		checkpoint['''model''']
  remove_ignore_keys_(snake_case							)
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		state_dict['''decoder.embed_tokens.weight'''].shape[0]

  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		{key.replace('''decoder'''       ,			'''model'''							): val for key, val in state_dict.items()}

  __SCREAMING_SNAKE_CASE      :	List[str]					=		XGLMConfig(
      vocab_size=snake_case       ,			max_position_embeddings=args.max_target_positions       ,			num_layers=args.decoder_layers       ,			attention_heads=args.decoder_attention_heads       ,			ffn_dim=args.decoder_ffn_embed_dim       ,			d_model=args.decoder_embed_dim       ,			layerdrop=args.decoder_layerdrop       ,			dropout=args.dropout       ,			attention_dropout=args.attention_dropout       ,			activation_dropout=args.activation_dropout       ,			activation_function='''gelu'''       ,			scale_embedding=not args.no_scale_embedding       ,			tie_word_embeddings=args.share_decoder_input_output_embed       ,			)

  __SCREAMING_SNAKE_CASE      :	Tuple					=		XGLMForCausalLM(snake_case							)
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		model.load_state_dict(snake_case       ,			strict=snake_case							)
  print(snake_case							)
  __SCREAMING_SNAKE_CASE      :	str					=		make_linear_from_emb(model.model.embed_tokens							)

  return model


if __name__ == "__main__":
   lowercase_ 				=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   lowercase_ 				=			parser.parse_args()
   lowercase_ 				=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)

from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge





@total_ordering
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __lt__(       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				return self[-1] < other[-1]



		def __eq__(       self     :     Tuple     ,  snake_case__     :     Union[str, Any]		)      ->	Union[str, Any]:
				return self[-1] == other[-1]



def 	lowerCamelCase					(	UpperCamelCase     :				list							)   ->    list:
		_lowerCamelCase				   =  []
		# sort into stacks
		for element in collection:
				_lowerCamelCase				   =  Stack([element]							)
				_lowerCamelCase				   =  bisect_left(UpperCamelCase			,       UpperCamelCase							)
				if i != len(UpperCamelCase							):
						stacks[i].append(UpperCamelCase							)
				else:
						stacks.append(UpperCamelCase							)

    # use a heap-based merge to merge stack efficiently
		_lowerCamelCase				   =  merge(*(reversed(UpperCamelCase							) for stack in stacks)							)
		return collection


if __name__ == "__main__":
				A						=			input('Enter numbers separated by a comma:\n').strip()
				A						=			[int(item) for item in user_input.split(',')]
				print(patience_sort(unsorted))